



"""simple docstring"""
import functools
from typing import Any



def 							lowerCAmelCase_    (				SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__						)      ->   bool:

		# Validation
		if not isinstance(_lowerCamelCase,			_lowerCamelCase						) or len(_lowerCamelCase						) == 0:
				raise ValueError("the string should be not empty string"						)

		if not isinstance(_lowerCamelCase,			_lowerCamelCase						) or not all(
		    isinstance(_lowerCamelCase,			_lowerCamelCase						) and len(_lowerCamelCase						) > 0 for item in words						):
				raise ValueError("the words should be a list of non-empty strings"						)

		# Build trie
		a_  :   Any			    =  {}
		a_  :   Union[str, Any]			    =  "WORD_KEEPER"

		for word in words:
				a_  :   Union[str, Any]			    =  trie
				for c in word:
						if c not in trie_node:
								a_  :   Tuple			    =  {}

						a_  :   Any			    =  trie_node[c]

				a_  :   Dict			    =  True

		a_  :   int			    =  len(_lowerCamelCase						)

		# Dynamic programming method
		@functools.cache
		def is_breakable(SCREAMING_SNAKE_CASE__						) -> bool:
				if index == len_string:
						return True

				a_  :   Dict			    =  trie
				for i in range(_lowerCamelCase,			_lowerCamelCase						):
						a_  :   str			    =  trie_node.get(string[i],			_lowerCamelCase						)

						if trie_node is None:
								return False

						if trie_node.get(_lowerCamelCase,			_lowerCamelCase						) and is_breakable(i + 1						):
								return True

				return False

		return is_breakable(0						)


if __name__ == "__main__":
					import doctest

					doctest.testmod()







"""simple docstring"""





import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression

# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split

# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures

# Importing the dataset
_SCREAMING_SNAKE_CASE   :							int          =		pd.read_csv(
    '''https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/'''
    '''position_salaries.csv'''
)
_SCREAMING_SNAKE_CASE   :							List[Any]          =		dataset.iloc[:, 1:2].values
_SCREAMING_SNAKE_CASE   :							str          =		dataset.iloc[:, 2].values


_SCREAMING_SNAKE_CASE       ,				_SCREAMING_SNAKE_CASE       ,				_SCREAMING_SNAKE_CASE       ,				_SCREAMING_SNAKE_CASE   :							Dict          =		train_test_split(X, y, test_size=0.2, random_state=0)


_SCREAMING_SNAKE_CASE   :							Optional[int]          =		PolynomialFeatures(degree=4)
_SCREAMING_SNAKE_CASE   :							Union[str, Any]          =		poly_reg.fit_transform(X)
_SCREAMING_SNAKE_CASE   :							List[str]          =		LinearRegression()
pol_reg.fit(X_poly, y)







def 	lowerCamelCase__			(				) ->							List[str]:
  plt.scatter(_lowerCamelCase						,							_lowerCamelCase						,							color='red'					)
  plt.plot(_lowerCamelCase						,							pol_reg.predict(poly_reg.fit_transform(_lowerCamelCase					)					)						,							color='blue'					)
  plt.title('Truth or Bluff (Linear Regression)'					)
  plt.xlabel('Position level'					)
  plt.ylabel('Salary'					)
  plt.show()


if __name__ == "__main__":
  viz_polymonial()

  # Predicting a new result with Polymonial Regression
  pol_reg.predict(poly_reg.fit_transform([[5.5]]))
  # output should be 132148.43750003




